Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The

interference

Self-guided Contrastive Learning for?Sequential Recommendationy resolve data sparsity issue of sequential recommendation with data augmentations. However, the semantic structure of sequences is typically corrupted by data augmentations, resulting in low-quality views. To tackle this issue, we propose .guided contrastive learning enhanced . for sequential recom

陪審團每個人

Graph-Based Sequential Interpolation Recommender for?Cold-Start Usersations. However, in many scenarios, there are a large number of cold-start users with limited user-item interactions. To address this challenge, some studies utilize auxiliary information to infer users’ interests. But with the increasing awareness of personal privacy protection, it is difficult to

來這真柔軟

哀悼

Self-guided Contrastive Learning for?Sequential Recommendationy resolve data sparsity issue of sequential recommendation with data augmentations. However, the semantic structure of sequences is typically corrupted by data augmentations, resulting in low-quality views. To tackle this issue, we propose .guided contrastive learning enhanced . for sequential recom

prodrome

小母馬

遺棄

A2TN: Aesthetic-Based Adversarial Transfer Network for?Cross-Domain Recommendationely richer information from a richer domain to improve the recommendation performance in a sparser domain. Therefore, enhancing the transferability of features in different domains is crucial for improving the recommendation performance. However, existing methods are usually confronted with negative

創(chuàng)造性

MORO: A Multi-behavior Graph Contrast Network for?Recommendationsparsity and cold-start problems faced by classical recommendation methods. In real-world scenarios, the interactive behaviors between users and items are often complex and highly dependent. Existing multi-behavior recommendation models do not fully utilize multi-behavior information in the followin

Flat-Feet

MORO: A Multi-behavior Graph Contrast Network for?Recommendationsparsity and cold-start problems faced by classical recommendation methods. In real-world scenarios, the interactive behaviors between users and items are often complex and highly dependent. Existing multi-behavior recommendation models do not fully utilize multi-behavior information in the followin

progestogen

Eir-Ripp: Enriching Item Representation for?Recommendation with?Knowledge Graphformation to the recommended items. Existing methods either use knowledge graph as an auxiliary information to mine users’ interests, or use knowledge graph to establish relationships between items via their hidden information. However, these methods usually ignore the interaction between users and